class_docstr: A generic wrapper for computing graph convolution on directed
  graphs as described in the `"Edge Directionality Improves Learning on
  Heterophilic Graphs" <https://arxiv.org/abs/2305.10498>`_ paper.
  :class:`DirGNNConv` will pass messages both from source nodes to target
  nodes and from target nodes to source nodes.
  
  Args:
      conv (MessagePassing): The underlying
          :class:`~torch_geometric.nn.conv.MessagePassing` layer to use.
      alpha (float, optional): The alpha coefficient used to weight the
          aggregations of in- and out-edges as part of a convex combination.
          (default: :obj:`0.5`)
      root_weight (bool, optional): If set to :obj:`True`, the layer will add
          transformed root node features to the output.
          (default: :obj:`True`)
code: def __repr__(self) -> str:
      return f"{self.__class__.__name__}({self.conv_in}, alpha={self.alpha})"
  
file_path: pytorch_geometric/torch_geometric/nn/conv/dir_gnn_conv.py
imports: import copy
  import torch
  from torch import Tensor
  from torch_geometric.nn.conv import MessagePassing
label_desc: Use image node_id 4 for calling the DirGNNConv obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, self, str, self, str